README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)
```
# clustRcompaR

The goal of clustRcompaR is to make it easy to cluster (or group) a series of documents (texts of any length), and to interpret these groups and to describe their frequency across factors, such as between different groups or over time. 

## Installation

You can install the development version of clustRcompaR from GitHub with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("alishinski/clustRcompaR")
```

You can install the stable release on CRAN with:

```{r, eval = F}
install.packages("clustRcompaR")
```

## Example

This is a basic example using the built-in inaugural addressess dataset. 

First, we use `cluster()` to cluster the documents into three clusters. We include a new variable, `year_before_1900`, which we will later use to compare frequencies across clusters. Then we use `extract_terms()` to view the terms and term frequencies in the two clusters.

First, let's process the texts.

```{r example}
library(clustRcompaR)
library(dplyr)

d <- inaugural_addresses
d <- mutate(d, century = ifelse(Year < 1800, "17th",
                                ifelse(Year >= 1800 & Year < 1900, "18th",
                                       ifelse(Year >= 1900 & Year < 2000, "19th", "20th"))))

```

Next, we **cluster** the texts.

```{r clustering}
three_clusters <- cluster(d, century, n_clusters = 3)
extract_terms(three_clusters)
```

Then, we use the `compare()` function to compare the frequency of clusters across a factor, in this case, the century. We can then use the `compare_plot()` or `compare_test()` (which uses a Chi-Square test) function. 

Here, we can **compare** the texts.

```{r}
three_clusters_comparison <- compare(three_clusters, "century")
compare_plot(three_clusters_comparison)
```